Data activation and AI are empowering financial institutions to deliver relevant, timely, and meaningful customer experiences.
Preetha, to start off, what initially drew you to the intersection of data, AI, and financial services, and how has your leadership journey shaped DeepTarget’s vision?
I’ve always believed that data—when used intelligently—has the power to transform how organizations operate. My background spans both deep tech and enterprise leadership, so the intersection of AI, data, and financial services was a natural progression. At DeepTarget, we saw a unique opportunity: regional and community financial institutions were sitting on incredibly rich, yet untapped, data assets. The problem wasn’t data collection. It was activation. That became our mission: to turn passive data into proactive engagement and measurable growth.
My leadership journey has reinforced one key truth—technology alone doesn’t drive transformation. It takes a clear vision, cultural alignment, and a relentless focus on delivering value to both institutions and their accountholders. That’s the lens through which we’ve shaped DeepTarget’s evolution.
Financial institutions are rich with customer data, yet many treat it passively. Why do you think so many organizations overlook the strategic potential of the data they already have?
Banks and credit unions are sitting on a goldmine of accountholder data—every transaction, every interaction, every financial behavior tells a story. Yet, too often, this data is treated as a byproduct of operations rather than as a strategic growth lever.
Why? Because legacy mindsets and systems have kept many institutions in a ‘data storage’ mode instead of ‘data activation.’ The focus historically has been on compliance, not customer intelligence. As a result, valuable data remains siloed, fragmented, and underused—while fintechs and digital-first competitors are moving fast, building their entire business models around advanced data analytics.
The truth is that many institutions resemble data hoarders more than data strategists. And in today’s environment, that’s no longer a neutral position, it’s a competitive disadvantage. The institutions that win will be those that turn their data into personalized experiences, predictive insights, and revenue-generating action.
What practical first steps can banks or credit unions take to begin turning their operational data into a growth engine?
The first step is clarity. Financial institutions don’t need to boil the ocean—they need to start with a candid assessment of where they are today. That means looking at data infrastructure, analytics capabilities, and how aligned the organization is around using data strategically. Is your data accessible? Is it clean and connected across systems? Do your teams have the tools—and the mindset—to act on insights?
Once you’ve established a baseline, the key is prioritization. Focus on quick wins that deliver measurable impact—whether that’s integrating core and digital banking data, launching a targeted campaign, or equipping frontline staff with relevant insights. You don’t have to transform everything at once. But you do have to start. The institutions that move with focus and intent will build momentum—and see data become a real engine for growth.
You’ve spoken about primary barriers that prevent financial institutions from effectively leveraging their data. What are those, and how can they be overcome?
The biggest barrier isn’t the lack of data—it’s the fragmentation of it. Most financial institutions are running on legacy systems that weren’t built to connect the dots. Deposits may live in one system and be accessible to some, lending in another, digital engagement somewhere else. That disconnect makes it nearly impossible to see the full picture of an accountholder’s financial life.
To overcome this, institutions need a strategy and infrastructure that makes data both accessible and actionable. That doesn’t always mean a core overhaul. Middleware solutions, smart integrations, and virtual data lakes can bridge the gap—allowing data from different systems to talk to each other without disrupting operations.
Ultimately, this is about unlocking value that already exists within your institution. When teams—from marketing to member services to lending—can access and act on unified insights, it changes the game. The blind spots disappear, and the opportunities come into focus.
In a competitive landscape filled with fintech disruptors and big banks, how is AI-powered personalization helping smaller institutions level the playing field?
AI is a great equalizer. Community banks and credit unions may not have the budgets of national banks, but they have something just as powerful—deep, trusted relationships and rich, long-term transaction data. When paired with AI, that data becomes a strategic asset.
AI goes beyond traditional analytics. It uncovers patterns and behaviors that humans would never spot—like identifying which accountholders are likely shopping for a home, or who might respond best to a new savings product. And importantly, AI doesn’t have to be built in-house. With the right fintech partners, smaller institutions can deploy powerful AI capabilities without building a data science team from scratch.
We’re seeing community institutions use AI to deliver hyper-personalized, timely messages at scale—often outperforming their larger counterparts. It’s no longer about size. It’s about smart execution. And AI is giving smaller players a serious edge.
Beyond marketing, where else are you seeing financial institutions apply personalization to improve the overall customer experience?
Personalization is beginning to make an impact beyond marketing where its presence is being explored in areas such as customer service, digital banking, and product design. In digital channels, we can see institutions being able to tailor dashboard content based on a member’s financial behavior — surfacing relevant tools like budgeting or savings goals based on recent transactions.
In branches or call centers, frontline staff equipped with AI-driven insights can have more meaningful, consultative conversations. Even product development becomes more data-informed, with financial institutions being able to provide tailored offerings — such as goal-based savings accounts or small business lending programs — that align closely with specific member segments’ behaviors and needs. This level of personalization will not only improve satisfaction but also deepens engagement.
What role does trust play in deploying AI within customer-facing platforms, and how can financial institutions maintain that trust while scaling personalization?
Trust is everything. Especially when you’re using AI to power personalization in financial services, where the stakes are high and the relationships are built on decades of credibility.
The key is transparency. Accountholders are generally open to their data being used—if it clearly benefits them and if they feel in control. That’s why institutions need to communicate not just what AI is doing, but why. Explain how it leads to better service, smarter recommendations, and more relevant experiences.
Equally important is governance. AI must operate within the guardrails of consumer protection, fair lending, and privacy regulations. Strong governance frameworks, ethical use of data, and clear preference management tools show your accountholders that personalization doesn’t come at the cost of privacy.
When institutions get this right, AI doesn’t erode trust—it strengthens it. Because the experience becomes more helpful, more relevant, and (paradoxically) more human.
Increasing wallet share is a goal for many institutions. How is data-driven personalization directly influencing customer engagement and product uptake?
Personalization turns passive banking into active relationship-building. When financial institutions use accountholder and transaction data to understand accountholder behavior—what they’re spending on, saving for, or researching—they gain powerful insight into real needs.
That insight fuels engagement. Instead of generic product pushes, institutions can reach out with timely, relevant offers—like pre-approvals for auto loans just as someone finishes paying off their current vehicle, or college savings options when tuition-related transactions start appearing. It’s not just marketing; it’s financial guidance that makes it personal.
This kind of intelligence drives deeper relationships. The more aligned your outreach is with someone’s financial journey, the more likely they are to respond, trust, and consolidate more of their financial life with your institution. That’s how personalization directly impacts wallet share—by showing up with the right solution at exactly the right moment.
Reducing churn is critical. What are some examples of how proactive, intelligent outreach—powered by AI—can help keep customers loyal?
Churn often doesn’t happen overnight—it builds over time. The warning signs are there: fewer logins, lower balances, changes in transaction patterns. The challenge is spotting them early enough to act. That’s where AI changes the game.
With intelligent data analysis, financial institutions can detect subtle shifts in behavior and trigger proactive outreach—before the customer walks away. For example, if someone starts making regular payments to a competitor’s credit card, you can respond with a targeted balance transfer offer, personalized down to their actual interest savings. That kind of relevance resonates—and retains.
Or consider a member whose engagement is gradually declining. Rather than wait for the account to go dormant, AI can flag the risk and prompt a check-in—sometimes that nudge alone is enough to re-engage them.
These aren’t guesswork campaigns. They’re precision tools powered by real-time insight. And when consumers feel seen and supported at key moments, loyalty grows.
As DeepTarget continues to evolve, what innovations are on the horizon that financial institutions should be preparing for now?
The next wave of innovation lies in making AI-powered personalization feel seamless and human. As digital engagement grows, financial institutions must invest in systems that can proactively anticipate and address accountholder needs without feeling invasive. This includes more dynamic, real-time engagement across channels, AI-assisted financial wellness tools, and next-best-action frameworks that help institutions serve members with relevance and empathy.
As we look ahead, we’re focused on helping institutions think strategically and act tactically. This means using data and AI to drive campaign-based marketing which evolves into continuous, intelligent engagement. The next step is to combine deep transaction data, behavioral signals, and AI to orchestrate meaningful experiences throughout the accountholder journey.
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